Responding to the February 2021 Texas Freeze: A Case Study of the Reaction to the Cascading Effects of a Complex Disaster.

  • Published In: Journal of Homeland Security & Emergency Management, 2024, v. 21, n. 1. P. 99 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Spraktes, Faith; McEntire, David A 3 of 3

Abstract

The following article examines the cascading effects that took place from February 11–20, 2021, through the perspectives of emergency managers, FEMA employees, and others who responded to "The Texas Freeze." The research discusses the literature on cascading disasters as well as the methodology that was utilized to conduct this study. It then examines the unique challenges experienced before and after the storm. In particular, the article explores the loss of power that subsequently resulted in the lack of water, the freezing of pipes, and flooding. In addition, it mentions other problems such as transportation and the provision of fuel as well as numerous consequences that posed considerable challenges for hospitals, long-term care facilities, and emergency managers. The article concludes with recommendations to strengthen infrastructure, mitigate winter storms, and increase planning and preparedness for complex disasters. The main point to be made is that far more consideration needs to be given to proactively understand and anticipate cascading disasters. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Homeland Security & Emergency Management. 2024/01, Vol. 21, Issue 1, p99
  • Document Type:Article
  • Subject Area:Power and Energy
  • Publication Date:2024
  • ISSN:1547-7355
  • DOI:10.1515/jhsem-2022-0025
  • Accession Number:175345864
  • Copyright Statement:Copyright of Journal of Homeland Security & Emergency Management is the property of De Gruyter and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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